{"paper_id":"acb1df3f-e0b7-4f7c-8bb0-c66a97a756bd","body_text":"Abstract\nObjective\nTo develop and evaluate a T2 MR–based radiomics prediction model incorporating radiomics features and clinical parameters to predict the response to magnetic resonance–guided focused ultrasound surgery (MRgFUS) in patients with adenomyosis.\nMaterials and methods\nSixty-nine patients (mean age, 38.6 years; age range, 26–50 years) with adenomyosis treated by MRgFUS were reviewed and allocated to training (n = 48) and testing cohorts (n = 21). One thousand one hundred eighteen radiomics features were extracted from T2-weighted imaging before MRgFUS. The radiomics features’ dimension was reduced by Pearson correlation coefficient after normalization. Analysis of variance and logistical regression were used for feature selection by fivefold cross-validation in the training cohort, and the machine learning model was constructed for comparing the clinical model, radiomics model, and radiomics-clinical model which combined survived radiomics features and clinical parameters. The discrimination result of the model was obtained by bootstrap; receiver operating characteristic curve, area under the curve (AUC), and decision curve analyses were performed to illustrate the model performance in both the training and testing cohorts.\nResults\nGood response was achieved in 47 patients (68.1%) and failed in 22 patients (38.9%). The radiomics model comprised four selected features and demonstrated a degree of prediction capability of patients’ poor response to MRgFUS treatment. The radiomics-clinical model showed good discrimination, with an AUC of 0.81 (95% confidence interval, 0.592–0.975) in the testing cohort. The decision curve analysis also showed favorable performance of the radiomics-clinical model.\nConclusions\nA prediction model composed of T2WI-based radiomics features and clinical parameters could be applied to guide the radiologist to evaluate MRgFUS for patients with adenomyosis who will achieve good response.\nKey Points\n• Magnetic resonance imaging–guided focused ultrasound surgery represents an alternative treatment for adenomyosis, but nearly one third of patients remain symptomatic 6 months after MRgFUS.\n• Combining four radiomics features of T2-weighted MRI with eight clinical features further improves prediction of poor responders to MR-guided focused ultrasound treatment of uterine adenomyosis (AUC = 0.81 in the testing cohort).\n• The radiomics model based on T2-weighted imaging combined with clinical parameters can help predict which patients are likely to have a good response to MRgFUS for adenomyosis.\nSimilar content being viewed by others\nAbbreviations\n- L 3mm3DGLCMinverse variance :\n-\nT2 log sigma 3–0-mm 3D GLCM inverse variance\n- W HLHFOmedian :\n-\nT2 wavelet HLH first-order median\n- W HLLFOminimum :\n-\nT2 wavelet HLL first-order minimum\n- W LLHNGTDMstrength :\n-\nT2 wavelet LLH NGTDM strength\n- AUC:\n-\nArea under the ROC curve\n- DCA:\n-\nDecision curve analysis\n- GR:\n-\nGood responders\n- HIFU:\n-\nHigh-intensity focused ultrasound\n- MRgFUS:\n-\nMagnetic resonance–guided focused ultrasound surgery\n- NRS:\n-\nNumerical rating scales (to assess pain severity)\n- NSAIDs:\n-\nNon-steroidal anti-inflammatory drugs\n- PR:\n-\nPoor responders\n- ROC:\n-\nReceiver operating characteristic curve\n- SSS:\n-\nSymptom severity score\n- UFS-QOL:\n-\nUterine fibroid symptom and quality of life\nReferences\nGaravaglia E, Audrey S, Annalisa I et al (2015) Adenomyosis and its impact on women fertility. 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Clin Cancer Res 23:7253–7262\nAcknowledgments\nThis research is financially supported by the National Natural Science Foundation of China (81871400, 81671740, 81771816, and 61731009), the Shanghai Academic/Technology Research Leader Program (17XD1424200), and the Excellent Academic Leaders of Shanghai of the Shanghai Municipal Commission of Health (2017BR038).\nFunding\nThis study has received funding from the National Natural Science Foundation of China, Shanghai Academic/Technology Research Leader Program, and Excellent Academic Leaders of Shanghai of the Shanghai Municipal Commission of Health.\nAuthor information\nAuthors and Affiliations\nCorresponding author\nEthics declarations\nGuarantor\nThe scientific guarantor of this publication is Han Wang.\nConflict of interest\nThe authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.\nStatistics and biometry\nOne of the authors has significant statistical expertise.\nInformed consent\nWritten informed consent was waived by the Institutional Review Board.\nEthical approval\nInstitutional Review Board approval was obtained.\nStudy subjects or cohorts overlap\nSome study subjects or cohorts have been previously reported in European Radiology.\nMethodology\n• retrospective\n• diagnostic or prognostic study/observational/performed at one institution\nAdditional information\nPublisher’s note\nSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.\nElectronic supplementary material\nESM 1 (download DOCX )\n(DOCX 137 kb)\nRights and permissions\nAbout this article\nCite this article\nLi, Z., Zhang, J., Song, Y. et al. Utilization of radiomics to predict long-term outcome of magnetic resonance–guided focused ultrasound ablation therapy in adenomyosis. Eur Radiol 31, 392–402 (2021). https://doi.org/10.1007/s00330-020-07076-1\nReceived:\nRevised:\nAccepted:\nPublished:\nVersion of record:\nIssue date:\nDOI: https://doi.org/10.1007/s00330-020-07076-1","source_license":"CC0","license_restricted":false}